Targeted e-commerce business strategies based on affiliation networks derived from predictive cognitive traits

ABSTRACT

Embodiments are directed to a computer implemented business campaign development system. The system includes an electronic tool configured to hold data of a user, and an analyzer circuit configured to derive a cognitive trait of the user based at least in part on the data of the user. The system further includes a targeted business strategy development system configured to derive a targeted business strategy based at least in part on the cognitive trait of the user.

BACKGROUND

The present disclosure relates in general to systems and methodologiesfor developing e-commerce business strategies. More specifically, thepresent disclosure relates to systems and methodologies for developingtargeted marketing-type and advertising-type e-commerce businessstrategies based on the identification and grouping of predictivecognitive traits from among a population.

The ability to target advertisements, in terms of both content andscope, to specific population segments is a fundamental requirement foreffective marketing and advertising campaigns. Marketing and advertisingbusiness strategies often involve an analysis of a population's tastesand needs based on information that members of the population sharethrough various electronic media. In e-commerce settings, for example,the analysis employed is often semantic, wherein what a user searches orwrites about is used to infer what a user needs. An example of asemantic-based advertising strategy is known generally as semantictargeting. Semantic targeting is a technique enabling the delivery oftargeted advertising for advertisements appearing on websites and isused by online publishers and advertisers to increase the effectivenessof their campaigns. The selection of advertisements is served byautomated systems based on the content displayed to the user.

Semantic-based marketing and advertising strategies typically involvescanning the content of web-pages to identify keywords. However, suchsystems are unable to identify the context of the entire page andtherefore the inferences drawn from such analysis is inherentlyimprecise. For example, the word “orange” can be a color, a fruit, atelecommunications company, a mountain bike, and countless othervariants.

Social network information is also used to analyze a population's tastesand needs. In a typical configuration, social network information isaggregated and associated statically with users and their socialnetworks. Social network information is limited in that the extracteddata is focused on users who have established connections to one anotherthrough the social network, and users outside of that social network arenot typically included.

Neither sematic-based marketing and advertising strategies nor socialnetwork information analysis techniques create categories of potentiale-commerce marketing or advertising population segments rapidly anddynamically, and independently from the limitations of traditionalsemantic-based or social network-based analyses.

SUMMARY

Embodiments are directed to a computer implemented business campaigndevelopment system. The system includes an electronic tool configured tohold data of a user, and an analyzer circuit configured to derive acognitive trait of the user based at least in part on the data of theuser. The system further includes a targeted business strategydevelopment system configured to derive a targeted business strategybased at least in part on the cognitive trait of the user.

Embodiments are further directed to a computer implemented method ofdeveloping a business campaign. The method includes storing, using amemory, data of a user, and deriving, using an analyzer circuit, acognitive trait of the user based at least in part on the data of theuser. The method further includes deriving, using a targeted businessstrategy development system, a targeted business strategy based at leastin part on the cognitive trait of the user.

Embodiments are further directed to a computer program product forimplementing a business campaign. The computer program product includesa computer readable storage medium having program instructions embodiedtherewith, wherein the computer readable storage medium is not atransitory signal per se. The program instructions are readable by aprocessor circuit to cause the processor circuit to perform a methodthat includes storing, by a memory of the processor circuit, data of auser. The method further includes deriving, using an analyzer circuit, acognitive trait of the user based at least in part on the data of theuser. The method further includes deriving, using a targeted businessstrategy development system, a targeted business strategy based at leastin part on the cognitive trait of the user.

Additional features and advantages are realized through techniquesdescribed herein. Other embodiments and aspects are described in detailherein. For a better understanding, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as embodiments is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments are apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings in which:

FIG. 1 depicts a cloud computing node according to one or moreembodiments;

FIG. 2 depicts a cloud computing environment according to one or moreembodiments;

FIG. 3 depicts abstraction model layers according to one or moreembodiments;

FIG. 4 depicts a diagram illustrating a system according to one or moreembodiments;

FIG. 5A depicts a graphical text analyzer's output feature vectorcomprising an ordered set of words or phrases, wherein each isrepresented by its own vector according to one or more embodiments;

FIG. 5B depicts a graph of communications according to one or moreembodiments of the disclosure;

FIG. 6 depicts various equations illustrating a core algorithm of agraphical text analyzer in accordance with one or more embodiments;

FIG. 7 depicts of a diagram of a graphical text analysis systemaccording to one or more embodiments;

FIG. 8 depicts a flow diagram of a methodology according to one or moreembodiments; and

FIG. 9 depicts a diagram of a computer program product according to oneor more embodiments.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with three digit reference numbers. The leftmost digits ofeach reference number corresponds to the figure in which its element isfirst illustrated.

DETAILED DESCRIPTION

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed. Additionally, although this disclosure includes adetailed description of analyzing text in order to derive parameters ofa marketing-type or advertising-type e-commerce businessstrategy/campaign development system, implementation of the teachingsrecited herein are not limited to marketing-type or advertising-typebusiness strategy/campaign development systems. Rather, embodiments ofthe present disclosure are capable of being implemented in conjunctionwith any other type of business strategy/campaign development system,now known or later developed, wherein the strategy/campaign is focusedand targeted based at least in part on the identification and groupingof communication targets using the identification, analysis and groupingof predictive cognitive traits from among a population.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows: Software as a Service (SaaS): thecapability provided to the consumer is to use the provider'sapplications running on a cloud infrastructure. The applications areaccessible from various client devices through a thin client interfacesuch as a web browser (e.g., web-based e-mail). The consumer does notmanage or control the underlying cloud infrastructure including network,servers, operating systems, storage, or even individual applicationcapabilities, with the possible exception of limited user-specificapplication configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and a cognitive trait based business strategymodule 96 for developing targeted business strategies/campaigns based atleast in part on an identified nexus between desired business outcomesand individuals and/or ad hoc population groups (e.g., ad hocaffiliation networks) having one or more particular cognitive traits incommon.

As previously noted herein, the targeting of marketing and/oradvertising to specific population segments is a fundamental requirementfor effective marketing and advertising campaigns. Marketing andadvertising business campaigns/strategies often involve an analysis of apopulation's tastes and needs based on information that members of thepopulation share through various electronic media. In e-commercesettings, for example, the analysis employed is often semantic, whereinwhat a user searches or writes about is used to infer what a user needs.An example of a semantic-based advertising strategy is known generallyas semantic targeting. Semantic targeting is a technique enabling thedelivery of targeted advertising for advertisements appearing onwebsites and is used by online publishers and advertisers to increasethe effectiveness of their campaigns. The selection of advertisements isserved by automated systems based on the content displayed to the user.

Semantic-based marketing and advertising strategies typically involvescanning the content of web-pages to identify keywords. However, suchsystems are unable to identify the context of the entire page andtherefore the inferences drawn from such analysis is inherentlyimprecise. For example, the word “orange” can be a color, a fruit, atelecommunications company, a mountain bike, and countless othervariants.

Social network information is also used to analyze a population's tastesand needs. In a typical configuration, social network information isaggregated and associated statically with users and their socialnetworks. Social network information is limited in that the extracteddata is focused on users who have established connections to one anotherthrough the social network, and users outside of that social network arenot typically included.

Neither sematic-based marketing and advertising strategies nor socialnetwork information analysis techniques create categories of potentiale-commerce marketing or advertising population segments rapidly anddynamically, and independently from the limitations of traditionalsemantic-based or social network-based analyses.

Turning now to an overview of the present disclosure, one or moreembodiments provide systems and methodologies for developing businessstrategies. More specifically, one or more embodiments of the presentdisclosure provide an e-commerce-based system and related methodologiesfor developing targeted marketing-type and advertisement-type businessstrategies based on the identification and/or grouping of marketingand/or advertising targets using the identification, analysis and/orgrouping of predictive cognitive traits from among a population. As usedin the present disclosure, a cognitive trait is defined as arepresentation of measures of a user's total behavior over some periodof time (including musculoskeletal gestures, speech gestures, internalphysiological changes, measured by imaging devices, microphones,physiological and kinematic sensors in a high dimensional measurementspace) within a lower dimensional feature space. Our preferredembodiment uses certain feature extraction techniques for identifyingcertain cognitive traits. Specifically, the reduction of a set ofbehavioral measures over some period of time to a set of feature nodesand vectors, corresponding to the behavioral measures' representationsin the lower dimensional feature space, is used to identify theemergence of a certain cognitive trait over that period of time. Therelationship of one feature node to other similar nodes through edges ina graph corresponds to the temporal order of transitions from one set ofmeasures and the feature nodes and vectors to another. Some connectedsubgraph of the feature nodes is herein defined as a cognitive trait. Wefurther describe the analysis, categorization, and identification ofthese cognitive traits by means of further feature analysis ofsubgraphs, including dimensionality reduction of the subgraphs, forexample by means of graphical analysis, which extracts topologicalfeatures and categorizes the resultant subgraph and its associatedfeature nodes and edges within a subgraph feature space.

As used in the present disclosure, the term e-commerce is not limited tofor-profit activities, and is intended to include activities such asphilanthropic, political, social, volunteer and the like. In accordancewith one or more embodiments, information in various forms (e.g., voice,text) is gathered through an electronic tool and/or web crawler from auser/client. The gathered information has emotional content (e.g.,empathy, understanding, speed, terseness, etcetera) from which cognitiveuser/client traits may be extracted by downstream components (e.g., agraphical text analyzer of analyzer circuit 410) of the e-commercesystem. These extracted cognitive traits may be utilized by otherdownstream components (e.g., affiliation networks and targeted businessstrategy systems) to develop and deliver targeted business strategiesbased at least in part on an identified nexus between desired businessoutcomes and individuals and/or ad hoc population groups (e.g., ad hocaffiliation networks) having one or more cognitive traits in common.Desired business outcomes may include a variety of outcomes, includingbut not limited to purchasing a product/service, joining a group,volunteering time to a political campaign, voting for a particularcandidate/referendum, writing letters of support, donating to a charityand the like.

Members of ad hoc affiliation networks developed according to thepresent disclosure may or may not know each other or have evercommunicated with each other. The commonality among members of thedisclosed ad hoc affiliation networks is based on the system of thepresent disclosure determining that the members of the ad hocaffiliation network have one or more identified cognitive traits incommon. Accordingly, business strategies, and particularly e-commercebased business strategies, developed in accordance with the presentdisclosure do not suffer from the inherent imprecision of semantic-basedmarketing and advertising strategy development systems, and further donot suffer from the limitations imposed by analyzing traditional socialnetworks that are limited in that the extracted data is focused on userswho have established connections to one another through the socialnetwork, and users outside of the social network are not typicallyincluded. Thus, business strategies, and particularly e-commerce basedbusiness strategies, developed in accordance with the present disclosurecreate categories of potential e-commerce marketing or advertisingpopulation segments rapidly and dynamically, and independently from thelimitations of traditional semantic-based or social network-basedanalyses.

At least the features and combinations of features described in theimmediately preceding paragraphs, including the corresponding featuresand combinations of features depicted in the FIGS., amount tosignificantly more than implementing a method of developing a businesscampaign in a particular technological environment. Additionally, atleast the features and combinations of features described in theimmediately preceding paragraphs, including the corresponding featuresand combinations of features depicted in the FIGS., go beyond what iswell-understood, routine and conventional in the relevant field(s).

Turning now to a more detailed description of the present disclosure,FIG. 4 depicts a diagram illustrating an e-commerce-based targetedbusiness strategy development and implementation system (e-commercesystem) 400 according to one or more embodiments. E-commerce system 400includes an electronic tool 402 having a user interface 404, variousinternet access points 408, an analyzer circuit 410, a graphconstructing circuit 412, a graphs repository 414, affiliation networks418, business systems 420, business strategy systems 422, businessstrategy implementation systems 424 and cloud computing system 50,configured and arranged as shown. A user or client 430 interfaces withe-commerce system 400 via user interface 404 of electronic tool 402.Cloud 50 may supplement, support or replace some or all of thefunctionality of electronic tool 402, analyzer circuit 410, graphconstructing circuit 412, graphs repository 414, affiliation networks418, business systems 420, business strategy systems 422 and businessstrategy implementation systems 424. Additionally, some or all of thefunctionality of electronic tool 402, analyzer circuit 410, graphconstructing circuit 412, graphs repository 414, affiliation networks418, business systems 420, business strategy systems 422 and businessstrategy implementation systems 424 may be implemented as a node 10(shown in FIGS. 1 and 2) of cloud 50.

In one or more embodiments, electronic tool 402 may be implemented as ane-commerce tool that accesses the internet (not shown) through internetaccess point 408. The term e-commerce refers to trading in products orservices using computer networks, such as the internet. E-commerce drawson technologies such as mobile commerce, electronic funds transfer,supply chain management, internet marketing, online transactionprocessing, electronic data interchange (EDI), inventory managementsystems, and automated data collection systems. Modern e-commercetypically uses the internet for at least one part of the transaction'slife cycle, although it may also use other technologies such as e-mail.E-commerce businesses employ a variety of system functionalities,including but not limited to online shopping web sites for retail salesdirect to consumers, providing or participating in online marketplacesthat process third-party business-to-consumer or consumer-to-consumersales, business-to-business buying and selling; gathering and usingdemographic data through web contacts and social media,business-to-business electronic data interchange, marketing toprospective and established customers by e-mail or fax (for example,with newsletters), and engaging in retail for launching new products andservices. However, as previously noted herein, the term e-commerce asused in the present disclosure is not limited to for-profit activities,and is intended to include activities such as philanthropic, political,social, volunteer and the like.

As previously noted herein, electronic tool 402 may include all of thefunctionality of node 10 (shown in FIGS. 1 and 2) of cloud 50.Electronic tool 402 sends data to and/or receives data from the internetthrough internet access point 408. Electronic tool 402 also sends datato and/or receives data from the internet through a web crawler (notshown). A web crawler is a program that visits web sites and reads theirpages and other information in order to create entries for a searchengine index. The major search engines on the web all have such aprogram, which is also known as a “spider” or a “bot.” Web crawlers aretypically programmed to visit sites that have been submitted by theirowners as new or updated. Entire sites or specific pages can beselectively visited and indexed. Web crawlers crawl through a site apage at a time, following the links to other pages on the site until allpages have been read.

Electronic tool 402 may further include functionality that allows it toreceive or gather communications (e.g., text, spoken words, video,emails) made by user/client 430 to user interface 404 (e.g., a graphicaluser interface (GUI) based keyboard, a touch screen, etc.). Forinstance, electronic tool 402 may include a mobile device such as asmartphone, a smartwatch, a tablet computer, a laptop computer,etcetera, as well as stationary devices such as a desktop computer, amainframe and the like. User interface 404 may include one or moremicrophones to receive audio communications made by user/client 430,along with one or more means of receiving textual communications fromuser/client 430, such as a virtual or physical keyboard or keypad.Electronic tool 402 may further include functionality that allows it toreceive or gather communications (e.g., customer reviews at web sites,emails, instant messages, tweets, phone calls, faxes, multimedia chats,Facebook content, etc.) mined by a web crawler. Electronic tool 402 mayalso convert any received audio communications into textualcommunications using one or more now known or later developedspeech-to-text techniques.

User/client 430 may be a person who interfaces with the internet for avariety of activities, including but not limited to purchasing productsand services, and may have personal and/or business needs and wants.User/client 430 may be a single individual or may represent severalrelated individuals, forming a “composite” customer or buyer of a goodor a service. For example, in one or more embodiments, the user/client430 may be one individual who is seeking to buy a product, informationon a product, help, guidance, instructions and the like. User/client 430may also, via user interface 404, express an opinion, provide a productreview, provide feedback, and/or receive advertising. In one or moreembodiments, user/client 430 may represent several individuals, forexample, individuals in a social network, on a team, etcetera. In thiscase, the composite implementation of user/client 430 (C) is representedby several individuals (C1, C2, C3, . . . ) using various weights (w1,w2, w3, . . . ), and the information from the individuals may beanalyzed according to the equation C=w1*C1+w2*C2+w3*C3. Often, a“primary” individual (e.g., C1) seeking to make a purchase at a web sitemay receive more weight (w) then his or her colleagues. Weights maydepend on various factors such as a person's position in a company, ameasure of network connectivity in a social network, etcetera.Furthermore, the weights may derive from a nonlinear function of timeand other factors pertaining to the individual, their role, theirnetwork connectivity, and intrinsic dynamics of models of theindividual. In this manner, the analysis of the “composite” user/client430 functions as an analysis of a super-organism or hive mind that isreflective of more than one individual.

In one or more embodiments, composite advertisers may also be formedfrom two or more advertisers and analyzed by e-commerce system 400(e.g., by analyzer circuit 410). Thus, a super-organism can take theform of user/client-and-advertiser (e.g., customer and advertiser) dyad,for which both the client and advertiser are analyzed as one unit. Inone or more embodiments, a super-organism can take the form ofuser/client 430 teamed with a helper artificial agent having naturallanguage processing capabilities that help user/client 430 makepurchasing decisions. For example, the artificial agent may be used byand act on behalf of a human user/client, such that the actualdownstream analysis performed by e-commerce system 400 (e.g., analyzercircuit 410) on linguistic output of an artificial agent that is ineffect a proxy for direct linguistic output from user/client 430.

Graph constructing circuit 412 receives from analyzer circuit 410 data(e.g., text) of the user/client communications that were received atelectronic tool 402. Graph constructing circuit 412 builds a graph fromthe received data. More specifically, in some embodiments wherein thereceived data is text data, the graph constructing circuit 412 extractssyntactic features from the received text and converts the extractedfeatures into vectors. These syntactic vectors may have binarycomponents for the syntactic categories such as verb, noun, pronoun,adjective, lexical root, etc. For instance, a vector [0, 1, 0, 0 . . . ]represents a noun-word in some embodiments.

Graph constructing circuit 412 may also generate semantic vectors fromthe received text using one or more now known or later developedtechniques (e.g., Latent Semantic Analysis and WordNet). The semanticcontent of each communication in the text may be represented by avector, of which the components are determined by singular valuedecomposition of word co-occurrence frequencies over a large database ofdocuments.

A graph generated by the graph constructing circuit 412 may be in theform of: G=(N, E, {hacek over (W)}) where the nodes N represent tokens(e.g., words or phrases), the edges E represent temporal precedence inthe device user's communications, and each node possesses a featurevector {hacek over (W)} defined in some embodiments as a direct sum ofthe syntactic and semantic vectors and additional non-textual featurevector (e.g., a predetermined vector for the identity of a person). Thatis, in some embodiments, the feature vector {hacek over (W)} is definedby the equation: {hacek over (W)}={hacek over (w)}_(sym)⊕{hacek over(w)}_(ssm)⊕{hacek over (w)}_(ntxt), where {hacek over (W)} is thefeature vector, {hacek over (w)}_(sym) is the syntactic vector, {hacekover (w)}_(ssm) is the semantic vector, and {hacek over (w)}_(ntxt) isthe non-textual features. Additional details of exemplary graphs 500,520 are shown in FIGS. 5A and 5B, which are described in greater detaillater in this disclosure.

Graph constructing circuit 412 updates the graphs as more text fromuser/client 430 is received from analyzer circuit 410 as user/client 430makes more communications. Graph constructing circuit 412 stores thegenerated graph(s) in graphs repository 414.

Analyzer circuit 410 performs a graphical text analysis on the graphgenerated by graph constructing circuit 412. As a specific example of agraphical text analysis, in some embodiments, analyzer circuit 410analyzes the graph G for the person generated by graph constructingcircuit 412 based on a variety of topological features. The variety offeatures includes graph-theoretical topological measures of the graphskeleton (i.e., a graph without features vectors: G_(sk)={N, E}) such asdegree distribution, density of small-size motifs, clustering,centrality, etc. Similarly, additional values may be extracted byincluding the features vectors for each node of the graph. One suchinstance is the magnetization of the generalized Potts model (e.g.,H=Σ_(n) E_(nm) {hacek over (W)}_(n)

{hacek over (W)}_(m)) such that temporal proximity (e.g., number ofedges between two nodes) and feature similarity are taken into account.These features, which incorporate the syntactic, semantic and dynamicalcomponents of the communications, are then combined as amulti-dimensional features vector {hacek over (F)} that represents asample. This feature vector is finally used to train a standardclassifier: M=M({hacek over (F)}_(train), C_(train)), to discriminatethe samples that belong to different conditions C, such that for eachsample the classifier estimates its condition identity based on theextracted features: C(sample)=M ({hacek over (F)}_(sample)). Additionaldetails of the various equations illustrating a core algorithm of agraphical text analysis function in accordance with one or moreembodiments are shown in FIG. 6 and described in more detail laterherein. Additionally, although illustrated separately in FIG. 4, thefunctionality of graph constructing circuit 412, graphs repository 414and analyzer circuit 410 may be provided in a single component.

In some cases, analyzer circuit 410 can determine the cognitive traitsof user/client 430 only with a confidence level (CL). When analyzercircuit 410 determines that the value of CL is below a threshold (TH)(i.e., CL<TH), a change in the output of business strategyimplementation systems 424 (e.g., type of advertisement, or a set ofsteps used in making a sale) may not take place. However, if CL>TH, achange in the output of business strategy implementation systems 424 maybe automatically triggered.

Additionally, if CL<TH, a confidence-increasing action may automaticallybe triggered, such as an analysis of other people in a social network ofuser/client 430 (e.g., people close to the user/client 430), an analysisof prior fragments of text and/or speech of user/client 430 (e.g.,person seeking to make a purchase), an analysis of a prior fragments oftext and/or speech of individuals in a social network of user/client430. Various weights may be assigned to the prior fragment. For example,the further into the past a fragment occurs, the lower the weights ofsuch fragments.

Analyzer circuit 410 may trigger other confidence-level-increasingactions automatically if CL<TH. For example, more public information maybe quickly obtained about user/client 430 such as posts made inFacebook, various public communications, an analysis of past buyingqueries, demographic information associated with user/client 430, andthe like. The use of such information may be approved in an opt-infashion so that user/client 430 gives permission to perform suchanalyses because he or she wishes to receive better and more usefulexposure to goods and services.

Another confidence-level-increasing action is to trigger e-commercesystem 400 (e.g., via electronic tool 402) to query the user/client 430about whether it is estimating a cognitive trait of user/client 430appropriately or correctly. For example, e-commerce system 400 mayinclude natural language processing question/answer (NLP Q/A)functionality and/or systems (e.g., within electronic tool 402) thatanswer natural language questions by querying data repositories andapplying elements of language processing, information retrieval andmachine learning to arrive at a conclusion. An example NLP Q/A systemand/or functionality is IBM'S DeepQA technology as described in U.S.Pat. No. 8,275,803, issued Sep. 25, 2012, which is assigned to theassignee of the present disclosure, and which is incorporated byreference herein in its entirety. Such a NLP Q/A system may askuser/client 430 if he or she is confused or angry in order to increasethe value of CL. For cases in which user/client 430 includes anartificial agent, the use of such extra information may be also approvedin an opt-in fashion so that the artificial agent (or its owners) givepermission to perform such analyses because the artificial agent willreceive more useful advertisements.

Affiliation networks 418 develop secondary, ad hoc networks ofindividuals, referred to herein as affiliation networks, based on thecognitive traits identified and clustered by analyzer circuit 410. Theaffiliation networks developed by affiliation network 418 is based onidentified cognitive traits and does not require that individuals in theaffiliation network know each other or have interacted in the past.Business systems 420 utilize both affiliation network data fromaffiliation networks 418 and cognitive trait data from analyzer circuit410 as inputs to a variety of business processes and/or functionsincluding but not limited marketing systems, merchandising systems,supply chain systems, and others. Business strategy systems 422 developbusiness strategies that are targeted based at least in part on anidentified nexus between desired business outcomes (e.g., purchasing aproduct or a service) and individuals and/or groups having one or morecognitive traits in common. Business strategy implementation system 424develops systems to implement business strategies that are targetedbased at least in part on an identified nexus between desired businessoutcomes (e.g., purchasing a product or a service) and individualsand/or groups having one or more particular cognitive trait. Businesssystems 420, business strategy systems 422 and business strategyimplementation systems 424 all have access to the internet throughinternet access point 408. Thus, the ability of e-commerce system 400 toidentify individuals and/or groups having one or more cognitive traitsin common enables business systems 420, business strategy systems 422and business strategy implementation systems 424 to identify a nexusbetween desired business outcomes and individuals and/or groups havingone or more cognitive traits in common, and further enables thesebusiness systems to plan and execute dynamic business strategies thatanticipate, exploit and closely link to the cognitive traits and theidentified nexus.

The overall functionality provided by business systems 420, businessstrategy systems 422 and business strategy implementation systems 424are identified collectively as a targeted business strategy developmentsystem (TBS) 440, which may take a wide variety of formats, and whichmay or may not include each function of business systems 420, businessstrategy systems 422 and business strategy implementation systems 424.For example, TBS 440 may be delivered to user/client 430 via electronictool 402 by an artificial (software) agent (e.g., an avatar, or a liveimage of a real person) that interacts with user/client 430. Thedecision to switch to, or make use of, a virtual world setting may bedepend on the analyses performed. Guidance and prompts used by anadvertiser may be switched or enhanced and may also relate to guidanceon how the seller or marketer may present information in terms of suchaspects as speed of presentation, vocal characteristics, word use inemails, emotionality, etcetera. The marketer and seller may be afriendly human, a team of humans, a NLP Q/A system, etcetera. Thecognitive traits of user/client 430 identified by e-commerce system 400are used to enhance such NLP Q/A responses, so that the presentedinformation or answers have higher value than that developed without thecognitive traits identified according to the present disclosure.

Based on the above analysis of cognitive traits of user/client 430, ifan alternative marketer or seller is selected, and if the alternativeagent is a Q&A artificial agent, information may be emotively conveyedto user/client 430 as useful. For example, the cognitive traits ofuser/client 430 may be transformed into data that additionallyrepresents a simulated emotional state and potentially transmitted usingan avatar in a virtual world. Data representing an avatar that expressesthe simulated emotional state may be generated and displayed. Also, aprosody analyzer enhances the interpretation of natural languageutterances. Cognitive traits may be distributed over a client/serverarchitecture such that the scope of emotion recognition processing taskscan be allocated on a dynamic basis based on processing resources,channel conditions, client loads etcetera. The partially processedprosodic data can be sent separately or combined with other speech datafrom electronic tool 402 and streamed to a server for a real-timeresponse. Training of the prosody analyzer with real world expectedresponses improves emotion modeling and the real-time identification ofpotential features such as emphasis, intent, attitude and semanticmeaning in the speaker's utterances.

TBS 440 (e.g., a marketer or a seller) may respond through voice, email,fax, chat messages associated with an avatar, instant messages,etcetera. The responses may be in real time (e.g., on a phone call) orasynchronous (e.g., as with emails). The analysis may be used to changeavatar characteristics (e.g., avatar appearance in a store, motions, andvocal characteristics in a virtual universe) and/or scenery (e.g., abuilding vs. a forest).

E-commerce system 400 may learn to be more effective. The effectivenessof systems 400 may be judged in the real shopping world comprising realstores. For example, it is possible to measure the effectiveness of anadvertisement presented on a mobile device. Information on the reactionto the advertisement is stored on the device and then accessed by aserver or the advertiser. The method involves tracking device location,presenting to the user an advertisement or point of interest, storingclick-throughs on the advertisement or point of interest to mark a firstsuccess, and monitoring device current position to determine whether thedevice reaches a location associated with the advertisement or point ofinterest to mark a second success, counting successes, and providingsuccess feedback to the advertiser, for example, through server queries.This information may also be fed into methodology 800 (shown in FIG. 8).

It is also possible to enhance the approach described herein using userresponses and using methods known in the art. For example, anadvertising analysis system may also provide a possibly optimal oreffective advertisement from an incoming advertisement having aplurality of modifiable advertisement elements and methods formanufacturing and using same. Analyzing each possible advertisementvariation of the advertisement, the advertising analysis system appliesmultivariate testing to identify the advertisement variations withselected combinations of advertisement elements as being optimal testcases and provides the identified advertisement variations as testadvertisements. User response to each test advertisement is compiled astest results during a predetermined test period. Based upon the testresults, the advertising analysis system performs multivariate testingto analyze the interrelation among the tested advertisement elements andextrapolates the test results to predict the effectiveness of eachadvertisement variation. The advertising analysis system therebyautomatically provides a predetermined number of the advertisementvariations with the optimal predicted effectiveness as themore-effective advertisements in a timely manner. This information maybe fed into methodology 800 (shown in FIG. 8).

Thus, e-commerce system 400 significantly expands the scope of data thatmay be used to both assess and inform current and future businessstrategy campaigns. Significantly, the additional data accessed bye-commerce system 400 provides access to an even wider range ofhistorical data of user/client 430 that may be provided to analyzercircuit 410 for feature extraction analysis. For example, cloud 50 mayprovide information from social graphs, emails, recorded interviews andconversations of user/client 430.

Additional details of more specific implementations of variouscomponents of e-commerce system 400 will now be described with referenceto FIGS. 5A to 8, wherein electronic tool 402 converts the differentforms of information of user/client 430 to text, and analyzer circuit410A (shown in FIG. 7) includes a graphical text analyzer 702 (shown inFIG. 7).

Referring now to FIG. 5A, there is depicted a graphical text analyzer'soutput feature vector in the form of a word graph 500 having an orderedset of words or phrases shown as nodes 502, 504, 506, each representedby its own features vector 510, 512, 514 according to one or moreembodiments. Each features vector 510, 512, 514 is representative ofsome additional feature of its corresponding node 502, 504, 506 in someword/feature space. Word graph 500 is useful to extract topologicalfeatures for certain vectors, for example, all vectors that point in theupper quadrant of the feature space of words. The dimensions of theword/feature space might be parts of speech (verbs, nouns, adjectives),or the dimensions may be locations in a lexicon or an online resource ofthe semantic categorization of words in a feature space such as WordNet,which is the trade name of a large lexical database of English. InWordNet, nouns, verbs, adjectives and adverbs are grouped into sets ofcognitive synonyms (synsets), each expressing a distinct concept.Synsets are interlinked by means of conceptual-semantic and lexicalrelations. The resulting network of meaningfully related words andconcepts can be navigated with a browser. WordNet is also freely andpublicly available for download from the WorldNet website,www.worldnet.princeton.edu. The structure of WordNet makes it a usefultool for computational linguistics and natural language processing.

FIG. 5B illustrates a graph 520 for a group of persons (e.g., threepersons depicted as black, grey and white nodes). Specifically, forexample, the nodes for a person are depicted in black, the nodes foranother person are depicted in white, and the nodes for yet anotherperson are depicted in grey. The graph 520 may be built for all personsin the group or constructed by combining graphs for individual persons.In some embodiments, the nodes of the graph 520 may be associated withidentities of the persons. In some embodiments, the analyzer circuit 410may discard or anonymize the graphs of communications stored in thegraphs repository 414 for reasons of privacy, after graphical textanalysis is performed on those graphs. Analysis of groups of users maybe useful in categorizing a user's cognitive trait within differentcontexts, for example while on a phone call with other specificindividuals.

FIG. 6 depicts Equations A-H, which illustrate features of a corealgorithm that may be implemented by analyzer circuit 410A (shown inFIG. 7) having a graphical text analysis module 702 (shown in FIG. 7)according to one or more embodiments. Analyzer module 410A shown in FIG.7 is an implementation of analyzer module 410 (shown in FIG. 4), whereintext input 720 receives text of user/client 430 (shown in FIG. 4). Thetext received at text input 720 may have been converted from some otherform, such as speech, to text. The functionality that converts other,non-text data of user/client 430 to text may be provided in electronictool 402, analyzer circuit 410 or as a stand-alone circuit.

Continuing with a description of Equations A-H of FIG. 6 includingselected references to corresponding elements of analyzer module 410Aand graphical text analysis module 702 shown in FIG. 7, text orspeech-to-text is fed into a standard lexical parser (e.g., syntacticfeature extractor 704 of FIG. 7) that extracts syntactic features, whichare converted to vectors. Such vectors can have binary components forthe syntactic categories verb, noun, pronoun, etcetera, such that thevector represented by Equation A represents a noun word.

The text is also fed into a semantic analyzer (e.g., semantic featureextractor 706 of FIG. 7) that converts words into semantic vectors. Theconversion into semantic vectors can be implemented in a number of ways,including, for example, the use of latent semantic analysis. Thesemantic content of each word is represented by a vector whosecomponents are determined by the singular value decomposition of wordco-occurrence frequencies over a large database of documents. As aresult, the semantic similarity between two words “a” and “b” can beestimated by the scalar product of their respective semantic vectorsrepresented by Equation B.

A hybrid graph is created in accordance with Equation C in which thenodes “N” represent words or phrases, the edges “E” represent temporalprecedence in the speech, and each node possesses a feature vector “W”defined as a direct sum of the syntactic and semantic vectors plusadditional non-textual features (e.g. the identity of the speaker) asgiven by Equation D.

The graph “G” of Equation C is then analyzed based on a variety offeatures, including standard graph-theoretical topological measures ofthe graph skeleton as shown by Equation E, such as degree distribution,density of small-size motifs, clustering, centrality, etcetera.Similarly, additional values can be extracted by including the featurevectors attached to each node. One such instance is the magnetization ofthe generalized Potts model as shown by Equation F such that temporalproximity and feature similarity are taken into account.

The features that incorporate the syntactic, semantic and dynamicalcomponents of speech are then combined as a multi-dimensional featuresvector “F” that represents the speech sample. This feature vector isfinally used to train a standard classifier according to Equation G todiscriminate speech samples that belong to different conditions “C,”such that for each test speech sample the classifier estimates itscondition identity based on the extracted features represented byEquation H.

FIG. 7 depicts a diagram of an analyzer circuit 410A having a graphicaltext analysis circuit 702 according to one or more embodiments. Analyzercircuit 410A is an implementation of analyzer circuit 410 (shown in FIG.4). Analyzer circuit 410A includes text input 720, a syntactic featureextractor 704, a semantic feature extractor 706, a graph constructor708, a graph feature extractor 710, a hybrid graph circuit 712, alearning engine 714, a predictive engine 716 and an output circuit 718,configured and arranged as shown. In general, graphical text analyzer702 functions to convert inputs from text input circuit 720 into hybridgraphs (e.g., word graph 500 shown in FIG. 5A), which is provided tolearning engine 714 and predictive engine 716. In addition to thegraphical text analyzer algorithm illustrated in FIG. 6 and describedabove, additional details of the operation of graphical text analyzer702 are available in a publication entitled “Speech Graphs Provide AQuantitative Measure Of Thought Disorder In Psychosis,” authored byMota, et al., and published by PLOS ONE, April 2012, Volume 7, Issue 4,the entire disclosure of which is incorporated by reference herein inits entirety.

As noted, graphical text analyzer 702 provides word graph inputs tolearning engine 714, and predictive engine 716, which constructspredictive features or model classifiers of the state of the individualin order to predict what the next state will be, i.e., the predictedbehavioral or psychological category of output circuit 718. Accordingly,predictive engine 716 and output circuit 718 may be modeled as Markovchains.

FIG. 8 depicts a flow diagram of a semantic-free methodology 800performed by e-commerce system 400 (shown in FIG. 4) according to one ormore embodiments. Although the operations of methodology 800 areillustrated in a particular order, it will be understood by persons ofordinary skill in the relevant art that the order of the illustratedoperations may be changed without departing from the teachings of thepresent disclosure. In addition, it will be understood by persons ofordinary skill in the relevant art that one or more of the illustratedoperations my omitted, and/or operations not shown may be incorporated,without departing from the teachings of the present disclosure.Methodology 800 begins at block 802 by converting user communications totext tokens, and block 804 analyzes the text tokens to extract cognitivetraits of users that may be predictive. Block 806 analyzes the cognitivetraits to identify affiliation networks of users based at least in parton the cognitive traits. Block 808 analyzes additional characteristicsand/or categories of users within and/or across affiliation networks.Block 810 develops and deploys targeted marketing plans and/or targetedadvertising based at least in part on additional characteristics and/orcategories of users within and/or across affiliation networks.

Thus it can be seen from the forgoing detailed description that one ormore embodiments of the present disclosure provide technical benefitsand advantages. Specifically, methodology 800 and e-commerce system 400analyze cognitive traits of users of e-commerce tools based on asemantic-free analysis of text input to these tools, for example reviewsof products. These categories then allow grouping of users based oncognitive types, without secondary information from social networks,etcetera. More specifically, speech to text and text may be input to ane-commerce tool and collected in real time as tokens from each user ofthe tool (for example a reviewer of a product; in sequence spoken orentered). Text tokens are analyzed by a graphical text analyzer usingmachine-learning tools that extract predictive features from tokensequences, and makes inferences about the category of the currentcognitive category of a user, their emotional state, and desires.Categories for each user are analyzed and compared, clustered, and usedto create affiliation networks of users based on cognitive types.Product purchases, positive reviews, and other feedback from a user arerelated through these affiliation networks to other users. Targetedmarketing plans and targeted advertising are deployed to related usersbased on the affiliation and the purchases and reviews within theirnetwork.

Advantageously, members of the ad hoc affiliation networks developedaccording to the present disclosure may or may not know each other orhave ever communicated with each other. The commonality among members ofthe disclosed ad hoc affiliation networks is based on the system of thepresent disclosure determining that the members of the ad hocaffiliation network have one or more identified cognitive traits incommon. Accordingly, business strategies, and particularly e-commercebased business strategies, developed in accordance with the presentdisclosure do not suffer from the inherent imprecision of semantic-basedmarketing and advertising strategy development systems, and further donot suffer from the limitations imposed by analyzing traditional socialnetworks that are limited in that the extracted data is focused on userswho have established connections to one another through the socialnetwork, and users outside of the social network are not typicallyincluded. Specifically, the ability of the disclosed e-commerce systemto identify individuals and/or groups having one or more cognitivetraits in common enables the various business systems to identify anexus between desired business outcomes and individuals and/or groupshaving one or more cognitive traits in common, and to therefore plan andexecute dynamic business strategies that anticipate, exploit and closelylink to the cognitive traits and the identified nexus. Thus, businessstrategies, and particularly e-commerce based business strategies,developed in accordance with the present disclosure create categories ofpotential e-commerce marketing or advertising population segmentsrapidly and dynamically, and independently from the limitations oftraditional semantic-based or social network-based analyses.

Referring now to FIG. 9, a computer program product 900 in accordancewith an embodiment that includes a computer readable storage medium 902and program instructions 904 is generally shown.

The present disclosure may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent disclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thepresent disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the presentdisclosure. As used herein, the singular forms “a”, “an” and “the” areintended to include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,element components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A computer system configured to identify andcommunicate with an ad hoc affinity network of individuals, the computersystem comprising: an electronic tool having a processor circuitcommunicatively coupled to a memory; wherein the processor circuit andthe memory are configured to hold text data of a user; wherein theprocessor circuit is configured to perform an ad hoc affinity networkidentification method comprising: using a graphical text analyzercircuit to derive cognitive trait data representing a cognitive trait ofthe user based at least in part on the text data of the user; whereinthe cognitive trait data comprises a representation of measures of theuser's total behavior over a period of time within a lower dimensionalfeature space; wherein the graphical text analyzer circuit comprises agraphical text analysis module communicatively coupled to a learningengine and a predictive engine; wherein the graphical text analysismodule of the graphical text analyzer circuit is configured to applyfeature extraction techniques to generate word graphs; and wherein thefeature extraction techniques comprise reducing the set of behavioralmeasures of the user over the period of time to a set of feature nodesand vectors that correspond to representations in the lower dimensionalfeature space of the set of behavioral measures of the user over theperiod of time; based at least in part on the learning engine receivingmultiple instances of the word graphs, applying, using the learningengine, classifier training techniques to the multiple instances of theword graphs to generate and continuously refine a cognitive trait modelof the user; based at least in part on the predictive engine receivingthe word graphs and the cognitive trait model of the user, predicting,using the predictive engine, a predicted cognitive trait of the user;wherein the predicted cognitive trait of the user comprises therepresentations in the lower dimensional feature space of the set ofbehavioral measures of the user over the period of time; anddetermining, using a decision engine of the computing system, based atleast in part on the predicted cognitive trait of the user, that theuser is a member of the ad hoc affinity network of individuals; whereinall members of the ad hoc affinity network of individuals share thepredicted cognitive trait; wherein the processor circuit is configuredto perform a communications method for generating communications for thead hoc affinity network identification, the communications methodcomprising: transforming the cognitive trait data into an artificialagent configured and arranged to communicate with the user in a mannerthat reflects the predicted cognitive trait of the user; identifying anexus between a desired outcome and the predicted cognitive trait thatis shared by all members of the ad hoc affinity network; generating acommunication targeted to the members of the ad hoc affinity networkbased at least in part on the nexus between the desired outcome and thepredicted cognitive trait that is shared by all members of the ad hocaffinity network; and providing the communication to the user using theartificial agent.
 2. The system of claim 1, wherein all members of thead hoc affinity network of individuals have been selected for andincluded in the ad hoc affinity network based on application of thefeature extraction techniques individually to each of a plurality ofusers.
 3. The system of claim 1, wherein the user comprises asuper-organism.
 4. The system of claim 3, wherein the super-organismcomprises the user and a merchant.
 5. The system of claim 3, wherein thesuper-organism comprises the user and an artificial agent.
 6. A computerprogram product for identifying and communicating with an ad hocaffinity network of individuals, the computer program productcomprising: a computer readable storage medium having programinstructions embodied therewith, wherein the computer readable storagemedium is not a transitory signal per se, the program instructionsreadable by a processor circuit to cause the processor circuit toperform a method comprising: storing, using a memory of the processorcircuit, text data generated by a user; deriving, using a graphical textanalyzer of the processor circuit, cognitive trait data representing acognitive trait of the user based at least in part on the text datagenerated by the user; wherein the cognitive trait data comprises arepresentation of measures of the user's total behavior over a period oftime within a lower dimensional feature space; wherein the graphicaltext analyzer circuit comprises a graphical text analysis modulecommunicatively coupled to a learning engine and a predictive engine;wherein the graphical text analysis module of the graphical textanalyzer circuit is configured to apply feature extraction techniques togenerate word graphs; wherein the feature extraction techniques comprisereducing the set of behavioral measures of the user over the period oftime to a set of feature nodes and vectors that correspond torepresentations in the lower dimensional feature space of the set ofbehavioral measures of the user over the period of time; based at leastin part on the learning engine receiving multiple instances of the wordgraphs, applying, using the learning engine, classifier trainingtechniques to the multiple instances of the word graphs to generate andcontinuously refine a cognitive trait model of the user; based at leastin part on the predictive engine receiving the word graphs and thecognitive trait model of the user, predicting, using the predictiveengine, a predicted cognitive trait of the user; wherein the predictedcognitive trait of the user comprises the representations in the lowerdimensional feature space of the set of behavioral measures of the userover the period of time; determining, using a decision engine of theprocessor system, based at least in part on the predicted cognitivetrait of the user, that the user is a member of the ad hoc affinitynetwork of individuals; wherein all members of the ad hoc affinitynetwork of individuals share the predicted cognitive trait; transformingthe cognitive trait data into an artificial agent configured andarranged to communicate with the user in a manner that reflects thepredicted cognitive trait of the user; identifying a nexus between adesired outcome and the predicted cognitive trait that is shared by allmembers of the ad hoc affinity network; generating a communicationtargeted to the members of the ad hoc affinity network based at least inpart on the nexus between the desired outcome and the predictedcognitive trait that is shared by all members of the ad hoc affinitynetwork; providing the communication to the user using the artificialagent.
 7. The computer program product of claim 6, wherein all membersof the ad hoc affinity network of individuals have been selected for andincluded in the ad hoc affinity network based on application of thecomputer implemented method of claim 1 individually to each of aplurality of users.
 8. The computer program product of claim 7, whereindetermining that the user is a member of the ad hoc affinity network ofindividual is further based at least in part on identifying a nexusbetween a desired outcome and the predictive cognitive trait that themembers of the ad hoc affinity network have in common.